Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Computer Vision

Oct 1

Learning to Reason with Compressed Context: Ground-Truth-Free Adaptation of OmniLLMs via Self-Distillation

arXiv:2609.39953v1 Announce Type: new Abstract: Omni-modal large language models (OmniLLMs) enable unified audio-video understanding, but their long multimodal token sequences make deployment computa...

By Jianghao Wang, Ke Meng, Jian Li, Chi Cheng, Longyu Qi, Liyin Liang, Yifeng Qian, Chunbo Lai, Yutian Lin, Zeyu Wang

llms efficiency multimodal benchmarks

arXiv Computer Vision

Oct 1

Image Classifiers are Efficient Self-Supervised Video Representation Learners

arXiv:2609.40347v1 Announce Type: new Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of r...

By Owais Iqbal, Sudipta Sarkar, Shyam Marjit, Omprakash Chakraborty, Anirban Chakraborty, Abir Das

llms rag computer-vision benchmarks

arXiv Computer Vision

Oct 1

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

arXiv:2601.08355v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for sa...

By Guo Cheng, Huang Li

llms agents computer-vision robotics multimodal benchmarks safety

arXiv Computer Vision

Oct 1

EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection

arXiv:2603.17343v3 Announce Type: replace Abstract: The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Tradition...

By Chenyang Zhu, Maorong Wang, Jun Liu, Ching-Chun Chang, Isao Echizen

llms agents reinforcement-learning multimodal benchmarks safety

arXiv Computer Vision

Oct 1

Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification

arXiv:2603.24528v2 Announce Type: replace Abstract: Vision-language models like CLIP are trained with the objective of aligning text and image pairs. Beyond text prompts alone, recent works show that...

By Dipam Goswami, Simone Magistri, Gido M. van de Ven, Bart{\l}omiej Twardowski, Andrew D. Bagdanov, Tinne Tuytelaars, Joost van de Weijer

llms rag computer-vision multimodal benchmarks safety

arXiv Computer Vision

Oct 1

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments

arXiv:2604.18484v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from co...

By Kangan Qian, ChuChu Xie, Yang Zhong, Jingrui Pang, Siwen Jiao, Sicong Jiang, Zilin Huang, Yunlong Wang, Kun Jiang, Mengmeng Yang, Hao Ye, Guanghao Zhang, Hangjun Ye, Guang Chen, Long Chen, Diange Yang

llms agents reinforcement-learning robotics multimodal benchmarks

arXiv Computer Vision

Oct 1

One Adapter, Every Resolution: Gated Low-Rank Adaptation for Remote Sensing VLMs

arXiv:2605.07562v2 Announce Type: replace Abstract: Remote sensing imagery spans ground sampling distances (GSDs) from centimeters to tens of meters, so both the visual evidence for a geographic conc...

By Song Zhang, Yanlong Chen, Yining Chen, Xiaowei Zhang, Yawei Li

llms fine-tuning multimodal

arXiv Computer Vision

Oct 1

EvoIR-Agent: Self-Evolving Image Restoration Agentic System via Experience-Driven Learning

arXiv:2605.22208v3 Announce Type: replace Abstract: Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly sele...

By Kailin Zhuang, Jiawei Wu, Zhi Jin

llms agents multimodal benchmarks

arXiv Computer Vision

Oct 1

Revisit to Segment: Working Memory Distillation for Reasoning Segmentation

arXiv:2609.34863v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have approached image segmentation by reasoning about visual content and predicting target locations. Thei...

By Cilin Yan, Yilun Qiu, Wanyang Zhang, Rui Zu, Xiaolong Jiang, Jiayin Cai, Yao Hu

llms computer-vision reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Oct 1

Arm2Air: Cross-Embodiment Skeleton Transfer for 3D Relay Formation

arXiv:2607.27627v2 Announce Type: replace-cross Abstract: Unmanned aerial vehicle (UAV) relay networks can restore connectivity after communication infrastructure is damaged. Urban relay placement is...

By Dohun Lee, Kyeonghyun Yoo, Seokmin Kim, Byongho Lee, Seungjoo Oh, Hwangnam Kim

llms robotics fine-tuning

arXiv AI

Oct 1

ActionGuard: Tool Call Authorization under Poisoned Skills

arXiv:2609.39450v1 Announce Type: cross Abstract: LLM-based agents extend their capabilities through third-party skills that provide task-specific instructions, scripts, and tool-use procedures. Howe...

By Jihun Han, Yejin Jang, Byung Il Kwak, Mee Lan Han

llms agents multimodal

arXiv AI

Oct 1

What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling

arXiv:2609.39967v1 Announce Type: cross Abstract: Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few...

By Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov, Inessa Fedorova, Dmitry Bocharov, Ivan Oseledets, Valerii Ternovskii

llms

arXiv AI

Oct 1

Less Data, Better Timing: Student-Curriculum Coupling for VLM On-Policy Distillation in Temporal Video Grounding

arXiv:2609.40055v1 Announce Type: cross Abstract: On-policy distillation (OPD) provides dense supervision directly on student-generated trajectories, making it an effective post-training strategy for...

By Jiacheng Qiu, Yunsoo Kim, Ruichen Xu, Jian Luo, Petar M. Djuri\'c, Sima Mofakham

llms efficiency multimodal benchmarks

arXiv AI

Oct 1

An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices

arXiv:2601.02641v2 Announce Type: replace Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as...

By Jeiyoon Park, Daehwan Lee, Changmin Yeo, Yongshin Han, Minseop Kim

llms fine-tuning benchmarks

arXiv AI

Oct 1

Learning What to Forget: Distributional Unlearning for LLM Representation Spaces

arXiv:2609.38929v1 Announce Type: new Abstract: Machine learning systems increasingly face the need to remove the influence of entire data domains, such as toxic language, harmful behavior, or topica...

By Pinaki Mohanty, Haoran Tang, Maggie Makar, Rajiv Khanna

llms

arXiv AI

Oct 1

Alleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering

arXiv:2609.38962v1 Announce Type: new Abstract: Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to est...

By Litian Liu, Qiqi Hou, Yubing Jian, Reza Pourreza, Mohammad Ghavamzadeh, Roland Memisevic, Yao Qin, Hong Cai

llms safety

arXiv AI

Oct 1

When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate

arXiv:2609.38964v1 Announce Type: new Abstract: Multi-agent debate (MAD) is often used to improve large language model (LLM) reasoning, but sequential debate is rarely a neutral aggregator of agents'...

By Duofeng Xu, Bryan Hooi, Dandan Qiao

llms agents safety

arXiv AI

Oct 1

Targeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting

arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mech...

By Liang Twist Shan, Tianyu Hu, Hao Yan, Yiqiao Zhong

llms

arXiv AI

Oct 1

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This...

By Chenyangguang Zhang, Malgorzata Gwiazda, Guanlong Jiao, Yuanchen Ju, Federico Tombari, Koushil Sreenath, Marc Pollefeys, Sunghwan Hong

llms agents reinforcement-learning robotics fine-tuning multimodal

arXiv AI

Oct 1

A helps B while B hurts A: directed transfer in instruction-tuning mixture

arXiv:2609.39702v1 Announce Type: new Abstract: Adapting a language model to a specialized corpus means choosing which instruction-tuning tasks to train on under a fixed budget, and testing one choic...

By Nima H. Siboni, Vahid Rostami

llms fine-tuning

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